Pulmonary diseases such as bronchiectasis, asthma, cystic fibrosis and Chronic Obstructive Pulmonary Disease (COPD) are characterized by abnormalities in airway dimensions, including airway wall thickness and lumen size (inner airway). Computed Tomography (CT) has become one of the primary means to depict and detect these abnormalities, as the availability of high-resolution near-isotropic data makes it possible to evaluate airways at oblique angles to the scanner plane. However, currently, clinical evaluation of airways is typically limited to subjective visual inspection only; systematic evaluation of the airways to take advantage of high-resolution data has not proved practical without automation.
Recently, automated methods have been proposed that are based on automatic extraction and modeling of the airway tree. After a tree model is obtained, an automated method can generate measurements of the airway dimensions, including wall thickness. The results can be classified as normal or abnormal. After this has been done, a model of the tree can be colored to depict normal and abnormal bronchi, or to depict severity. As a result, a 3D view of the bronchial tree can be created that has differing colorings for thickened walls vs. normal walls.
However, airway walls may be thicker due to inflammatory disease processes or to other processes such as scarring. Furthermore, inflammatory causes of thickening may be potentially treatable (such as with anti-inflammatory agents) whereas scarring type processes are less likely to respond to treatment. Hence it would be valuable to know whether observed wall thickening is due to an inflammatory process, in order to better individualize patient treatment.
In previous experiments, it was shown that some airway walls in patients with airway disease experience iodine uptake following the administration of intravenous iodinated contrast. This iodine uptake in bronchial walls is believed to be caused by an increase in local blood flow, which in turn is caused by inflammation. Furthermore our experiments have also shown that the amount of iodine uptake may be estimated with Dual Energy Computed Tomography (DECT) imaging. The result of DECT imaging is an iodine map that depicts the amount of iodine uptake at any given location in the volume. Given a segmentation of the bronchial walls, a 3D view of the bronchial tree can be created that has differing colors for high uptake vs. low uptake amounts.
The challenge for physicians is to (1) rapidly identify thickened airway walls; and (2) distinguish thickened walls with high iodine uptake—indicating inflammatory disease—from those with little to no iodine—indicating non-inflammatory thickening. Accordingly, it is desired to provide a system for an interactive user interface that allows physicians to make just such a distinction.